This monograph presents a comprehensive exploration of Reverse Engineering of Deceptions (RED) in the field of adversarial machine learning. It delves into the intricacies of machine and human-centric attacks, providing a holistic understanding of how adversarial strategies can be reverse-engineered to safeguard AI systems.
For machine-centric attacks, reverse engineering methods for pixel-level perturbations are covered, as well as adversarial saliency maps and victim model information in adversarial examples. In the realm of human-centric attacks, the focus shifts to generative model information inference and manipulation localization from generated images.
In this work, a forward-looking perspective on the challenges and opportunities associated with RED are presented. In addition, foundational and practical insights in the realms of AI security and trustworthy computer vision are provided.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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